ELECTRICAL AND ELECTRONICS ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP4336	Introduction to Data Mining	Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Recommended Optional Program Components:	None
Course Objectives:	In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis.

Learning Outcomes

The students who have succeeded in this course;
1. Be able to understand Data Gathering and Pre-processing
2. Become familiar with Frequent Item Set Detection
3. Be able to understand Association Rule Mining
4. Be able to understand Classifiers, and their benefits
5. Be able to use Clustering
6. Be able to understand Clustering Evaluation

Course Content

1.Introduction to Basic Concepts
2.Data Exploration
3.Classification
4.Clustering
5.Dimensionality Reduction
6.Frequent Item Set Mining
7.Association Rule Mining

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Basic Concepts	None
2)	Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis	None
3)	Data Pre-Processing, Transformation, Normalization, Standardization	None
4)	Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation	None
5)	Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers
6)	Classification	None
6)	Classification and Regression: Artificial Neural Networks, Support Vector Machines
7)	Midterm I	Review of all topics covered so far
8)	Clustering: Partitioning and Hierarchical Algorithms	None
9)	Clustering: Density-Based Algorithms
10)	Cluster Evaluation, Comparing Clusterings	None
11)	Midterm II	none
12)	Dimensionality Reduction	none
13)	Frequent Item Set Mining	none
14)	Association Rule Mining	none

Sources

Course Notes / Textbooks:	Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar
References:	Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 20
Project	1	% 20
Midterms	2	% 20
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Study Hours Out of Class	16	32
Project	5	15
Homework Assignments	6	12
Midterms	8	28
Final	6	26
Total Workload		155

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and electric-electronic engineering subjects; ability to use theoretical and applied information in these areas to model and solve engineering problems.
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4)	Ability to devise, select, and use modern techniques and tools needed for electrical-electronic engineering practice; ability to employ information technologies effectively.
5)	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
6)	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7)	Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing.
8)	Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9)	Awareness of professional and ethical responsibility.
10)	Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11)	Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.